Density-based Input Sampling for Explanation (DISE) is a second novel approach for whitebox XFR introduced in this paper. DISE is an extension of Randomized Input Sampling for Explanation (RISE) [1]} using a prior density to aid in sampling.
Previous work [1]}[3]} has constructed a saliency map associated with a particular class by randomly perturbing the input image by masking selected pixels, evaluating it using a blackbox system, and accumulating those perturbations based on how confident the system is that the modified input image corresponds to the target class. However, these approaches generate masks to occlude the input image uniformly at random. This sampling process is inefficient, and can be improved by introducing a prior distribution to guide the sampling. In this section, we describe the extension to RISE [1]} where the prior density for input sampling is derived from a whitebox EBP with triplet loss.
